Before I can dive deeply into more ambitious projects, I need a more basic understanding of how PyTorch works, so this repo captures notebooks and experiments based on the PyTorch tutorials, and Ian Pointer's book Programming PyTorch for Deep Learning, using jupyter notebooks.
Using Anaconda makes things easy: use the Python 3.x command line installer.
After downloading and installing, execute these commands inside the (base) environment (the last one is optional, but useful if using AWS):
conda upgrade conda
conda update --all
conda install pytorch torchvision torchtext torchaudio -c pytorch
conda install tensorboard
conda install -c conda-forge awscli